Gait analysis device and computer program product

ABSTRACT

According to an embodiment, a gait analysis device includes a measuring unit configured to measure a subject&#39;s motion; a determining unit configured to determine a walking start point in time at which the subject starts walking based on the subject&#39;s motion; a feature quantity calculator configured to, when the walking start point in time is determined, calculate a feature quantity of the subject&#39;s motion measured during a predetermined time period starting from the walking start point in time as a time period in which the subject&#39;s motion is not stabilized; and an estimating unit configured to estimate a subject&#39;s walking condition based on the feature quantity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2011-238246, filed on Oct. 31, 2011; theentire contents of which are incorporated herein by reference.

FIELD

An embodiment described herein relates generally to a gait analysisdevice and a computer program product.

BACKGROUND

Devices that evaluate gait motion using various kinds of sensor deviceshave been developed for the purpose of observing the course of adisease, preventing falls, and the like. In medical practice, a subjectis required to do an action (standing on one foot or the like) needingbalance ability, and the subject's behavior is observed to determine afall risk.

However, in the related art, measurement and estimation of a diseasedegree are performed under the assumption that the subject keepswalking, and so it is difficult to estimate a walking condition and afall risk in a short time from a start of walking. In addition, thetechnique executed in the medical practice has a problem in that thesubject has a risk and feels pressured since the subject is required todo an action needing balance ability.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a hardware configuration diagram of a gait analysis deviceaccording to an embodiment;

FIG. 2 is a schematic diagram illustrating a mounting state of a gaitanalysis device according to an embodiment;

FIG. 3 is a functional block diagram illustrating a gait analysis deviceaccording to an embodiment;

FIG. 4 is a graph illustrating a relation among acceleration, adetermination result, and an analysis target;

FIG. 5 is a graph illustrating an analysis example of a feature quantitycalculation;

FIG. 6 is a table illustrating labels of three classes learned as aclassification example;

FIG. 7 is a conceptual diagram illustrating an output example of anoutput unit; and

FIG. 8 is a flowchart illustrating an operation of a gait analysisdevice according to an embodiment.

DETAILED DESCRIPTION

According to an embodiment, a gait analysis device includes a measuringunit configured to measure a subject's motion; a determining unitconfigured to determine a walking start point in time at which thesubject starts walking based on the subject's motion; a feature quantitycalculator configured to, when the walking start point in time isdetermined, calculate a feature quantity of the subject's motionmeasured during a predetermined time period starting from the walkingstart point in time as a time period in which the subject's motion isnot stabilized; and an estimating unit configured to estimate asubject's walking condition based on the feature quantity.

An embodiment will be described in detail with reference to theaccompanying drawings. FIG. 1 is a diagram illustrating a configurationof a gait analysis device 1 according to the embodiment. As illustratedin FIG. 1, the gait analysis device 1 includes a main body unit 10 and awearing unit 30.

The main body unit 10 includes a control unit 12, a storage unit 14, aninput unit 16, an output unit 18, a detecting unit 20, and acommunication unit 22. The control unit 12 includes, for example, acentral processing unit (CPU) 120, and controls respective componentsconstituting the main body unit 10. The storage unit 14 includes a readonly memory (ROM), a random access memory (RAM), and the like, which arenot illustrated in the drawing, and stores therein a program executed bythe control unit 12, data used for the control unit 12 to execute aprogram, and the like. Further, a storage medium 140 such as a memorycard having a function of transmitting/receiving a program and datato/from the storage unit 14 is detachably attached to the main body unit10.

The input unit 16 includes, for example, an input key or a switch, andreceives a user's input to the main body unit 10. The output unit 18includes, for example, a display unit 180 such as a liquid crystalpanel, a speaker 182 that outputs a sound or the like, and a vibrator(not illustrated). The output unit 18 outputs a state of the main bodyunit 10 and a processing operation result of the main body unit 10through at least one of a screen display, a sound, and vibration. Theinput unit 16 may be integrated with the display unit 180 through atouch panel.

The detecting unit 20 includes, for example, a tri-axial accelerationsensor having a sampling frequency of 128 Hz, and has an accelerationmeasurement range of, for example, ±6 G or more. For example, when thegait analysis device 1 is worn on a gait analysis subject, the detectingunit 20 detects acceleration in a vertical direction, acceleration in amoving direction of the subject, and acceleration in a horizontaldirection (a left-right direction) almost orthogonal to the movingdirection of the subject (acceleration in three directions).

The communication unit 22 includes a general-purpose interface thatperforms communication with the outside, and is configured to beconnectable to, for example, any one of wired communication,long-distance wireless communication, and near field communication(NFC).

For example, the wearing unit 30 is configured with a belt or the likewound on the subject's waist, and for example, the main body unit 10 isworn near the subject's waist.

FIG. 2 is a schematic diagram illustrating a state in which the gaitanalysis device 1 is worn near the subject's waist and directions ofacceleration measured by the gait analysis device 1. As illustrated inFIG. 2, when worn near the subject's waist, the gait analysis device 1detects acceleration in the vertical direction (a Y direction),acceleration in the moving direction of the subject (a Z direction), andacceleration in the horizontal direction (an X direction) almostorthogonal to the moving direction of the subject.

Next, a function of the gait analysis device 1 will be described. FIG. 3is a block diagram illustrating an outline of a function of the gaitanalysis device 1. FIG. 4 is a graph illustrating acceleration datadetected by the detecting unit 20, a determination result of adetermining unit 42 which will be described later, and an analysistarget extracted by an extracting unit 44.

As illustrated in FIG. 3, the gait analysis device 1 includes ameasuring unit 40, the determining unit 42, the extracting unit 44, ananalyzing unit 46, an estimating unit 48, and the output unit 18. Theoutput unit 18 illustrated in FIG. 3 corresponds to the output unit 18illustrated in FIG. 1.

The measuring unit 40 detects the subject's motion. Specifically, themeasuring unit 40 includes the detecting unit 20, and detects, forexample, acceleration in three directions which changes according thesubject's motion and measures the subject's motion (acceleration). Forexample, when power of the main body unit 10 is turned on, the measuringunit 40 continuously measures acceleration in three directions which isused to output a result of estimating the subject's walking conditionthrough the gait analysis device 1. For example, all acceleration datameasured by the measuring unit 40 is stored in the storage unit 14(FIG. 1) together with a time until no longer necessary.

The determining unit 42 determines whether or not the subject hasstarted walking based on the measurement result of the measuring unit40. Specifically, the determining unit 42 first acquires accelerationdata stored in the storage unit 14, and calculates a variance ofacceleration data measured by the measuring unit 40 at predeterminedtime intervals (at first time intervals a) within a predetermined timeframe (a first setting time period A). For example, the determining unit42 calculates a variance of acceleration data measured by the measuringunit 40 newly for 0.5 seconds (A=0.5 seconds) at intervals of 0.5seconds (a=0.5 seconds) as illustrated in FIG. 4. Here, when thesampling frequency of the detecting unit 20 is 128 Hz, the measuringunit 40 measures 64 pieces of acceleration data per direction within thefirst setting time period A (A=0.5 seconds). The first time interval “a”is preferably 0.5 seconds or less. The first setting time period A maybe set to be longer than the first time interval “a”.

Next, when a time period in which the variance of the acceleration datais equal to or less than a predetermined threshold value (thresholdvalue σ) lasts for a second setting time period B or more and then atime period in which the variance of the acceleration data is largerthan the threshold value σ lasts for a third setting time period C ormore, the determining unit 42 (FIG. 3) determines, as a walking startpoint in time at which the subject has started walking, a point in timeat which the variance of the acceleration data exceeds the thresholdvalue σ (or alternatively, a point in time at which it is lastlydetermined that the variance of the acceleration data is equal to orless than the threshold value σ). For example, the threshold value σ isset to 0.04. Preferably, each of the second setting time period B andthe third setting time period C has a value having a range of 2 secondsto 6 seconds.

For example, as illustrated in FIG. 4, when a time period in which thevariance of the acceleration data is equal to or less than thresholdvalue σ lasts for 6 seconds (the second setting time period B=6 seconds)or more and then a time period in which the variance of the accelerationdata is larger than the threshold value σ lasts for 6 seconds (the thirdsetting time period C=6 seconds) or more, the determining unit 42retroactively determines, as the walking start point in time at whichthe subject has started walking, a point in time at which the varianceof the acceleration data exceeds the threshold value σ (a start timingof the third setting time period C). In the example illustrated in FIG.4, at 214 seconds (timed by the measuring unit 40), the determining unit42 determines that the subject has started walking at 208 seconds. Inthis way, the determining unit 42 determines the walking start point intime, and determines whether or not the subject has started walking.Here, the determining unit 42 may be configured to calculate a standarddeviation instead of a variance.

Here, when the determining unit 42 determines that the subject hasstarted walking, the extracting unit 44 (FIG. 3) extracts data set foranalysis from the measurement result of the measuring unit 40.Specifically, when the determining unit 42 determines that the subjecthas started walking, the extracting unit 44 extracts the accelerationdata measured by the measuring unit 40 from acceleration data stored inthe storage unit 14 during an extraction time period, that is, until afourth setting time period D elapses from the walking start point intime. For example, the fourth setting time period D (the extraction timeperiod) is set to 3 seconds.

In addition, the extracting unit 44 also extracts a plurality of piecesof acceleration data measured by the measuring unit 40 from theacceleration data stored in the storage unit 14 during a plurality ofother extraction time periods obtained by sequentially delaying a starttiming of the extraction time period from the walking start point intime by a predetermined time interval (a second time interval b).

For example, as illustrated in FIG. 4, when the determining unit 42determines that the subject has started walking, the extracting unit 44extracts the acceleration data measured by the measuring unit 40 duringa extraction time period D1, that is, until 3 seconds (the fourthsetting time period D=3 seconds) elapse from the walking start point intime. Further, the extracting unit 44 extracts six sets of accelerationdata measured by the measuring unit 40. For example, during six otherextraction time periods D2 to D7 obtained by sequentially delaying thestart timing of the extraction time period from the walking start pointin time by an interval of 0.5 seconds (the second time interval b=0.5).In the following, when there is no need to distinguish among theextraction time periods D1 to D7, the extraction time periods D1 to D7may be collectively referred to simply as an extraction time period D(or the fourth setting time period D). Then, the acceleration dataextracted by the extracting unit 44 is corresponded to data set foranalysis used in the analyzing unit 46.

The analyzing unit 46 (FIG. 3) includes a feature quantity calculatingunit 460 and a variation value calculating unit 462. The featurequantity calculating unit 460 calculates a feature quantity ofacceleration data extracted by the extracting unit 44. Specifically, thefeature quantity calculating unit 460 receives the data during theperiod of analysis extracted by the extracting unit 44, calculates, forexample, an autocorrelation function of acceleration data in thevertical direction for each extraction time period D, for example, asillustrated in FIG. 5, and sets a first peak value (or a second peakvalue) as a maximum autocorrelation value. The feature quantitycalculating unit 460 may be configured to filter the data sets to passlow frequency components in the data sets and then calculate theautocorrelation function. In FIG. 5, the extraction time period D issimply denoted by D, and the extraction time periods D1 to D7 are simplydenoted by D1 to D7, respectively. The feature quantity calculating unit460 outputs, as the feature quantity of each extraction time period D,each calculated maximum autocorrelation value to the variation valuecalculating unit 462 and the estimating unit 48.

The autocorrelation function calculated by the feature quantitycalculating unit 460 has a value other than zero (0) when theacceleration data has periodicity, and has characteristics of which thevalue increases as the amplitude of acceleration data increases andnoise decreases. For example, as illustrated in FIG. 5, the maximumautocorrelation value in the extraction time period D1 is smaller thaneach value in the extraction time periods D2 to D7. In other words, theextraction time period D1 is a transition state between a state (forexample, a still state) in which the subject does not a dynamic motionand a state in which motion is stabilized. Therefore, in the extractiontime period D1, the amplitude of acceleration data is considered to besmall and the periodicity is considered to be low.

The feature quantity calculating unit 460 may be configured tocalculate, as the feature quantity, left-right symmetry of the data setsfor analysis extracted by the extracting unit 44 among the accelerationdata in the left-right direction detected by the detecting unit 20. Forexample, the feature quantity calculating unit 460 may calculate, as thefeature quantity, a mean value of the acceleration data in theleft-right direction (the X direction) detected by the detecting unit 20in each extraction time period D.

The variation value calculating unit 462 (FIG. 3) calculates a numericalvalue (a variation value) serving as an index of a variation in themaximum autocorrelation value of each the extraction time period D,which is calculated by the feature quantity calculating unit 460. Forexample, the variation value calculating unit 462 calculates a varianceor a standard deviation of the maximum autocorrelation value (see avariation illustrated in FIG. 5) of each extraction time period D or avalue using the sum of differences between the maximum autocorrelationvalue of the extraction time period D used as a reference and themaximum autocorrelation values of the other the extraction time periodsD as the variation value. Here, when the subject's motion is stabilized,the variation in the maximum autocorrelation value is considered todecrease.

The variation value calculating unit 462 may be configured to calculatea variation value of the left-right symmetry of the analysis targetextracted by the extracting unit 44 with respect to the acceleration inthe left-right direction detected by the detecting unit 20. For example,the variation value calculating unit 462 may calculate, as the variationvalue, a variance of the mean values of the acceleration data in theleft-right direction (the X direction), which is calculated by thefeature quantity calculating unit 460 in each extraction time period D.

Here, since a subject having excellent balance ability early enter astate of steady gate, in case of a subject having excellent balanceability, a variation in the feature quantity of each extraction timeperiod D is considered to promptly decrease.

The estimating unit 48 receives the feature quantity calculated by thefeature quantity calculating unit 460 and the variation value calculatedby the variation value calculating unit 462, and estimates the subject'swalking condition using at least one of the received feature quantityand the variation value.

For example, the estimating unit 48 receives, as inputs, the maximumautocorrelation value of the extraction time period D1 and the varianceof the maximum autocorrelation values of the extraction time periods D1to D7, and classifies the subject's walking condition into three classesof “safe,” “careful (cautious),” and “high-risk” using a classifier.

For example, the estimating unit 48 uses an algorithm of a supportvector machine (SVM) as the classifier. The SVM is a two-class patternclassification technique that performs non-linear classification byemploying a kernel function. However, the estimating unit 48 implementsthree-class classification by using an extension method using aplurality of classifiers such as a one-against-one technique and aone-against-all technique for the purpose of multi-class classification.

For example, the estimating unit 48 classifies the subject's walkingconditions into three classes illustrated in FIG. 6. Labels (classlabels) of “safe”, “careful”, and “high-risk” are attached to the threeclasses, respectively.

Here, the label will be described in detail. As illustrated in FIG. 6,for example, each label is associated with a score range representingbalance ability. The score range associated with each label correspondsto a berg balance scale (BBS) score. The BBS refers to a balance testincluding 14 kinds of actions such as standing on one foot and turning,and assesses a balance ability of an evaluation subject with a score of0 to 56 points (each of 14 kinds of actions is scored from 0 to 4).

The estimating unit 48 is trained based on previously measured dataprospectively. In advance, a feature quantity and a variation ofacceleration data during gait of subjects including persons withrelatively high risk of fall and persons with high balance ability areprepared as training data. The scores using the BBS for the samesubjects are measured in advance. The estimating unit 48 is trained toassociate the feature quantity and the variation value with a scorerange representing a walking condition.

In other words, the estimating unit 48 performs learning a relationbetween the three labels to classify a walking condition and the featurequantity and the variation value in advance, and classifies (estimates)the subject's walking condition based on the feature quantity and thevariation value of the subject whose walking condition is newlyanalyzed.

The estimating unit 48 may be configured to estimate the subject'swalking condion using at least one of the feature quantity and thevariation value. The estimating unit 48 may be configured to estimatethe walking condition by simply setting a threshold value to the featurequantity and the variation value without using a learning algorithm. Theestimating unit 48 may use a neural network as another algorithm or maybe configured to perform dimensional compression using a self-organizingmap, kernel principal component analysis (kernel PCA), or the like andthen execute a pattern recognition algorithm.

The output unit 18 (FIG. 3) receives and outputs the estimation resultof the estimating unit 48. FIG. 7 is a conceptual diagram illustratingan output example of the output unit 18. As illustrated in FIG. 7, theoutput unit 18 outputs the label representing the estimation result ofthe estimating unit 48 through a signal such as an image on a display ora sound. The output unit 18 may be configured to output at least one ofthe calculation result of the analyzing unit 46 and the estimationresult of the estimating unit 48.

Next, an operation of the gait analysis device 1 will be described. FIG.8 is a flowchart of an operation when the gait analysis device 1according to an embodiment executes a program corresponding to thefunction illustrated in FIG. 3. For example, the gait analysis device 1is powered on in a state (for example, a rest state) in which a subjectdoes not make a dynamic behavior such as walking and then starts gaitanalysis.

Referring to FIG. 8, in step S100, for example, when the gait analysisdevice 1 is powered on, the detecting unit 20 starts to detect thesubject's acceleration.

In step S102, the determining unit 42 determines whether or not thefirst time interval “a” (that is interval “a” in FIG. 4) has elapsed.Here, when the determining unit 42 determines that the first timeinterval “a” has elapsed (Yes in step S102), the process proceeds tostep S104. However, when it is determined that the first time interval“a” has not elapsed (No in step S102), the process of step S102 iscontinuously performed.

In step S104, the determining unit 42 calculates a variance ofacceleration data measured by the measuring unit 40, for example, withinthe first setting time period A.

In step S106, the determining unit 42 determines whether or not thevariance of the acceleration data calculated in the process of step S104is equal to or less than a predetermined threshold value (thresholdvalue σ). Here, when the determining unit 42 determines that thevariance of the acceleration data is equal to or less than the thresholdvalue σ (Yes in step S106), the process to proceed to step S108.However, when it is determined that the variance of the accelerationdata is larger than the threshold value σ (No in step S106), the processproceeds to step S112. Alternatively, the process may proceed to stepS102 and then continued.

In step S108, the determining unit 42 determines whether or not a timeperiod in which the variance of the acceleration data is equal to orless than the threshold value σ lasts for a second setting time period Bor more. Here, when the determining unit 42 determines that the timeperiod in which the variance of the acceleration data is equal to orless than the threshold value σ lasts for the second setting time periodB or more (Yes in step S108), the process proceeds to step S110.However, when it is determined that the time period in which thevariance of the acceleration data is equal to or less than the thresholdvalue σ does not last for the second setting time period B or more (Noin step S108), the process proceeds to step S102.

In step S110, the determining unit 42 determines whether or not thevariance of the acceleration data is larger than the threshold value σ.Here, when the determining unit 42 determines that the variance of theacceleration data is larger than the threshold value σ (Yes in stepS110), the process proceeds to step S112. However, when it is determinedthat the variance of the acceleration data is equal to or less than thethreshold value σ (No in step S110), the process proceeds to step S102.

In step S112, the determining unit 42 determines whether or not a timeperiod in which the variance of the acceleration data is larger thanthreshold value σ lasts for a third setting time period C or more. Here,when the determining unit 42 determines that the time period in whichthe variance of the acceleration data is larger than threshold value σlasts for the third setting time period C or more (Yes in step S112),the process proceeds to step S114. However, when the determining unit 42determines that the time period in which the variance of theacceleration data is larger than threshold value σ does not last for thethird setting time period C or more (No in step S112), the processproceeds to step S102.

In step S114, the determining unit 42 determines a walking start pointin time.

In step S116, the extracting unit 44 receives the determination resultof the determining unit 42 and extracts an analysis target.

In step S118, the feature quantity calculating unit 460 receives thedata set for analysis extracted by the extracting unit 44 and calculatesa feature quantity.

In step S120, the variation value calculating unit 462 receives thefeature quantity calculated by the feature quantity calculating unit460, and calculates a variation value.

In step S122, the estimating unit 48 receives at least one of thefeature quantities calculated by the feature quantity calculating unit460 and the variation value calculated by the variation valuecalculating unit 462, and then estimates the subject's walkingcondition.

In step S124, the output unit 18 outputs the estimation resultcalculated by the estimating unit 48.

Meanwhile, the gait analysis device 1 may be configured to estimate thesubject's walking condition using all acceleration in three directionsdetected by the detecting unit 20 (or using an arbitrary combination ofacceleration in three directions) or may be configured to estimate thesubject's walking condition using at least one of the feature quantityand the variation value calculated based on acceleration data in onedirection. The above-described embodiment has been described inconnection with the example in which the feature quantity calculated bythe feature quantity calculating unit 460 is distinguished from thevariation value calculated by the variation value calculating unit 462.However, the gait analysis device 1 may be configured to estimate thesubject's walking condition by regarding the variation value calculatedby the variation value calculating unit 462 as one of featurequantities. In other words, the gait analysis device 1 may be configuredto estimate the subject's walking condition by regarding all valuescalculated by the analyzing unit 46 as feature quantities correspondingto the subject's motion.

Further, in the gait analysis device 1, when the subject's walkingcondition is estimated based on the acceleration data of the extractiontime period D1, the same time period (3 seconds in the exampleillustrated in FIG. 4) as the extraction time period D1 is set as aninitial time period in which the subject's motion is not stabilized.Further, when the subject's walking condition is estimated based on theacceleration data of the extraction time periods D1 to D7. Theextraction time period is a time period (6 seconds in the exampleillustrated in FIG. 4) between the walking start point in time and theend point of the extraction time period D7 and is set as a time periodin which the subject's motion is not stabilized firstly.

The gait analysis device 1 is not limited to the configuration describedin the above-described embodiment. For example, the gait analysis device1 may be configured such that the main body unit 10 includes thedetecting unit 20 (or the measuring unit 40) and the communication unit22, and a personal computer (PC) or the like connected to the main bodyunit 10 via a network includes the determining unit 42, the extractingunit 44, the analyzing unit 46, the estimating unit 48, and the outputunit 18.

The main body unit 10 may be attached directly on the subject's bodywith the wearing unit 30, and may be attached with an adhesive member ormay be mounted to a backpack or the like.

The program executed by the gait analysis device 1 of the presentembodiment is configured to include modules for implementing theabove-described components (the determining unit 42, the extracting unit44, the analyzing unit 46, and the estimating unit 48).

According to the above-described embodiment, even though a single sensoris used, since a walking condition is estimated based on a featurequantity when a subject starts walking, the subject's walking conditioncan be estimated in a short period of time. In addition, according tothe embodiment, a fall risk can be estimated even though the subjectdoes not do a complicated action with a high risk and a heavy burden.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. A gait analysis device, comprising: a measuringunit configured to measure a subject's motion; a determining unitconfigured to determine a walking start point in time at which thesubject starts walking based on the subject's motion; a feature quantitycalculator configured to, when the walking start point in time isdetermined, calculate a feature quantity of the subject's motionmeasured during a predetermined time period starting from the walkingstart point in time as a time period in which the subject's motion isnot stabilized; and an estimating unit configured to estimate asubject's walking condition based on the feature quantity.
 2. The deviceaccording to claim 1, wherein the estimating unit estimates thesubject's walking condition through a pattern recognition algorithm thatreceives, as an input, the feature quantity calculated by the featurequantity calculator and outputs one of a plurality of class labelshaving different fall risk levels.
 3. The device according to claim 1,wherein the measuring unit measures acceleration data, which changes inresponse to the subject's motion, in at least one direction.
 4. Thedevice according to claim 3, wherein when a time period in which avariance of acceleration data measured by the measuring unit at a firsttime interval during a first setting time period is equal to or lessthan a predetermined threshold value lasts for a second setting timeperiod or more, and then a time period in which the variance of theacceleration is larger than the threshold value lasts for a thirdsetting time period or more, the determining unit determines, as thewalking start point in time, a point in time at which the variance ofthe acceleration exceeds the threshold value.
 5. The device according toclaim 4, wherein the measuring unit measures at least one ofacceleration in a vertical direction of the subject and acceleration ina horizontal direction almost orthogonal to a moving direction of thesubject, and the feature quantity calculator calculates a featurequantity of acceleration measured by the measuring unit until a fourthsetting time period elapses from the walking start point in time.
 6. Thedevice according to claim 5, wherein the feature quantity calculatorunit calculates, as the feature quantity, at least one of a maximumautocorrelation value in a autocorrelation function of the accelerationin the vertical direction measured by the measuring unit and an averagevalue of the acceleration in the horizontal direction.
 7. The deviceaccording to claim 5, wherein the feature quantity calculator calculatesa feature quantity of acceleration measured by the measuring unit duringa plurality of time periods obtained by sequentially delaying the fourthsetting time period from the walking start point in time by a secondtime interval.
 8. The device according to claim 7, wherein the featurequantity calculator calculates, as the feature quantity, either or bothof a plurality of maximum autocorrelation values in an autocorrelationfunction of the acceleration in the vertical direction measured duringthe plurality of time periods and a plurality of average values of theacceleration in the horizontal direction, the gait analysis devicefurther comprises a variation value calculator configured to calculateat least one of a variation value of the plural maximum autocorrelationvalues and a variation value of the plural average values, and theestimating unit estimates the subject's walking condition based on atleast one of the variation value and the feature quantity.
 9. The deviceaccording to claim 8, wherein the estimating unit estimates thesubject's walking condition through a pattern recognition algorithm thatreceives, as an input, at least one of the feature quantity and thevariation value and outputs at least one of a plural class labels havingdifferent fall possibility levels.
 10. The device according to claim 9,further comprising an output unit configured to output at least one ofthe feature quantity, the variation value and an estimation result ofthe estimating unit.
 11. A compute program product comprising a computerreadable medium including programmed instructions, wherein theinstructions, when executed by a computer, cause the computer toexecute: determining a walking start point in time at which a subjectstarts walking based on a subject's motion; calculating, when thewalking start point in time is determined, a feature quantity of thesubject's motion measured during a predetermined time period startingfrom the walking start point in time as a time period in which thesubject's motion is not stabilized; and estimating a subject's walkingcondition based on the feature quantity.